train_densenet.prototxt

name: "deep-net"
layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  hdf5_data_param{
    source: "../train.list"
    batch_size: 3
    shuffle: true
  }
  transform_param {
    crop_size_l : 64
    crop_size_h : 64
    crop_size_w : 64
  }
}
#########################################

layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "data"
  top: "Convolution1"
  convolution_param {
    engine: CUDNN
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "BatchNorm1"
  type: "BatchNorm"
  bottom: "Convolution1"
  top: "BatchNorm1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale1"
  type: "Scale"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
  relu_param {
    engine: CUDNN
  }
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "BatchNorm1"
  top: "Convolution2"
  convolution_param {
    engine: CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout1"
  type: "Dropout"
  bottom: "Convolution2"
  top: "Dropout1"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat1"
  type: "Concat"
  bottom: "Convolution1"
  bottom: "Dropout1"
  top: "Concat1"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm2"
  type: "BatchNorm"
  bottom: "Concat1"
  top: "BatchNorm2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale2"
  type: "Scale"
  bottom: "BatchNorm2"
  top: "BatchNorm2"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "BatchNorm2"
  top: "BatchNorm2"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "BatchNorm2"
  top: "Convolution3"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout2"
  type: "Dropout"
  bottom: "Convolution3"
  top: "Dropout2"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat2"
  type: "Concat"
  bottom: "Concat1"
  bottom: "Dropout2"
  top: "Concat2"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm3"
  type: "BatchNorm"
  bottom: "Concat2"
  top: "BatchNorm3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale3"
  type: "Scale"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU3"
  type: "ReLU"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution4"
  type: "Convolution"
  bottom: "BatchNorm3"
  top: "Convolution4"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout3"
  type: "Dropout"
  bottom: "Convolution4"
  top: "Dropout3"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat3"
  type: "Concat"
  bottom: "Concat2"
  bottom: "Dropout3"
  top: "Concat3"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm4"
  type: "BatchNorm"
  bottom: "Concat3"
  top: "BatchNorm4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale4"
  type: "Scale"
  bottom: "BatchNorm4"
  top: "BatchNorm4"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU4"
  type: "ReLU"
  bottom: "BatchNorm4"
  top: "BatchNorm4"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution5"
  type: "Convolution"
  bottom: "BatchNorm4"
  top: "Convolution5"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout4"
  type: "Dropout"
  bottom: "Convolution5"
  top: "Dropout4"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat4"
  type: "Concat"
  bottom: "Concat3"
  bottom: "Dropout4"
  top: "Concat4"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm5"
  type: "BatchNorm"
  bottom: "Concat4"
  top: "BatchNorm5"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale5"
  type: "Scale"
  bottom: "BatchNorm5"
  top: "BatchNorm5"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU5"
  type: "ReLU"
  bottom: "BatchNorm5"
  top: "BatchNorm5"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution6"
  type: "Convolution"
  bottom: "BatchNorm5"
  top: "Convolution6"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout5"
  type: "Dropout"
  bottom: "Convolution6"
  top: "Dropout5"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat5"
  type: "Concat"
  bottom: "Concat4"
  bottom: "Dropout5"
  top: "Concat5"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm6"
  type: "BatchNorm"
  bottom: "Concat5"
  top: "BatchNorm6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale6"
  type: "Scale"
  bottom: "BatchNorm6"
  top: "BatchNorm6"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU6"
  type: "ReLU"
  bottom: "BatchNorm6"
  top: "BatchNorm6"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution7"
  type: "Convolution"
  bottom: "BatchNorm6"
  top: "Convolution7"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout6"
  type: "Dropout"
  bottom: "Convolution7"
  top: "Dropout6"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat6"
  type: "Concat"
  bottom: "Concat5"
  bottom: "Dropout6"
  top: "Concat6"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm7"
  type: "BatchNorm"
  bottom: "Concat6"
  top: "BatchNorm7"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale7"
  type: "Scale"
  bottom: "BatchNorm7"
  top: "BatchNorm7"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU7"
  type: "ReLU"
  bottom: "BatchNorm7"
  top: "BatchNorm7"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution8"
  type: "Convolution"
  bottom: "BatchNorm7"
  top: "Convolution8"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout7"
  type: "Dropout"
  bottom: "Convolution8"
  top: "Dropout7"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat7"
  type: "Concat"
  bottom: "Concat6"
  bottom: "Dropout7"
  top: "Concat7"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm8"
  type: "BatchNorm"
  bottom: "Concat7"
  top: "BatchNorm8"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale8"
  type: "Scale"
  bottom: "BatchNorm8"
  top: "BatchNorm8"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU8"
  type: "ReLU"
  bottom: "BatchNorm8"
  top: "BatchNorm8"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution9"
  type: "Convolution"
  bottom: "BatchNorm8"
  top: "Convolution9"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout8"
  type: "Dropout"
  bottom: "Convolution9"
  top: "Dropout8"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat8"
  type: "Concat"
  bottom: "Concat7"
  bottom: "Dropout8"
  top: "Concat8"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm9"
  type: "BatchNorm"
  bottom: "Concat8"
  top: "BatchNorm9"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale9"
  type: "Scale"
  bottom: "BatchNorm9"
  top: "BatchNorm9"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU9"
  type: "ReLU"
  bottom: "BatchNorm9"
  top: "BatchNorm9"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution10"
  type: "Convolution"
  bottom: "BatchNorm9"
  top: "Convolution10"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout9"
  type: "Dropout"
  bottom: "Convolution10"
  top: "Dropout9"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat9"
  type: "Concat"
  bottom: "Concat8"
  bottom: "Dropout9"
  top: "Concat9"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm10"
  type: "BatchNorm"
  bottom: "Concat9"
  top: "BatchNorm10"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale10"
  type: "Scale"
  bottom: "BatchNorm10"
  top: "BatchNorm10"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU10"
  type: "ReLU"
  bottom: "BatchNorm10"
  top: "BatchNorm10"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution11"
  type: "Convolution"
  bottom: "BatchNorm10"
  top: "Convolution11"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout10"
  type: "Dropout"
  bottom: "Convolution11"
  top: "Dropout10"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat10"
  type: "Concat"
  bottom: "Concat9"
  bottom: "Dropout10"
  top: "Concat10"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm11"
  type: "BatchNorm"
  bottom: "Concat10"
  top: "BatchNorm11"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale11"
  type: "Scale"
  bottom: "BatchNorm11"
  top: "BatchNorm11"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU11"
  type: "ReLU"
  bottom: "BatchNorm11"
  top: "BatchNorm11"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution12"
  type: "Convolution"
  bottom: "BatchNorm11"
  top: "Convolution12"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout11"
  type: "Dropout"
  bottom: "Convolution12"
  top: "Dropout11"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat11"
  type: "Concat"
  bottom: "Concat10"
  bottom: "Dropout11"
  top: "Concat11"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm12"
  type: "BatchNorm"
  bottom: "Concat11"
  top: "BatchNorm12"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale12"
  type: "Scale"
  bottom: "BatchNorm12"
  top: "BatchNorm12"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU12"
  type: "ReLU"
  bottom: "BatchNorm12"
  top: "BatchNorm12"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution13"
  type: "Convolution"
  bottom: "BatchNorm12"
  top: "Convolution13"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout12"
  type: "Dropout"
  bottom: "Convolution13"
  top: "Dropout12"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat12"
  type: "Concat"
  bottom: "Concat11"
  bottom: "Dropout12"
  top: "Concat12"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm13"
  type: "BatchNorm"
  bottom: "Concat12"
  top: "BatchNorm13"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale13"
  type: "Scale"
  bottom: "BatchNorm13"
  top: "BatchNorm13"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU13"
  type: "ReLU"
  bottom: "BatchNorm13"
  top: "BatchNorm13"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution14"
  type: "Convolution"
  bottom: "BatchNorm13"
  top: "Convolution14"
  convolution_param {
    engine:CUDNN
    num_output: 160
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout13"
  type: "Dropout"
  bottom: "Convolution14"
  top: "Dropout13"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Pooling1"
  type: "Pooling"
  bottom: "Dropout13"
  top: "Pooling1"
  pooling_param {
    engine:CUDNN
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "BatchNorm14"
  type: "BatchNorm"
  bottom: "Pooling1"
  top: "BatchNorm14"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale14"
  type: "Scale"
  bottom: "BatchNorm14"
  top: "BatchNorm14"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU14"
  type: "ReLU"
  bottom: "BatchNorm14"
  top: "BatchNorm14"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution15"
  type: "Convolution"
  bottom: "BatchNorm14"
  top: "Convolution15"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout14"
  type: "Dropout"
  bottom: "Convolution15"
  top: "Dropout14"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat13"
  type: "Concat"
  bottom: "Pooling1"
  bottom: "Dropout14"
  top: "Concat13"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm15"
  type: "BatchNorm"
  bottom: "Concat13"
  top: "BatchNorm15"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale15"
  type: "Scale"
  bottom: "BatchNorm15"
  top: "BatchNorm15"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU15"
  type: "ReLU"
  bottom: "BatchNorm15"
  top: "BatchNorm15"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution16"
  type: "Convolution"
  bottom: "BatchNorm15"
  top: "Convolution16"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout15"
  type: "Dropout"
  bottom: "Convolution16"
  top: "Dropout15"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat14"
  type: "Concat"
  bottom: "Concat13"
  bottom: "Dropout15"
  top: "Concat14"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm16"
  type: "BatchNorm"
  bottom: "Concat14"
  top: "BatchNorm16"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale16"
  type: "Scale"
  bottom: "BatchNorm16"
  top: "BatchNorm16"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU16"
  type: "ReLU"
  bottom: "BatchNorm16"
  top: "BatchNorm16"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution17"
  type: "Convolution"
  bottom: "BatchNorm16"
  top: "Convolution17"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout16"
  type: "Dropout"
  bottom: "Convolution17"
  top: "Dropout16"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat15"
  type: "Concat"
  bottom: "Concat14"
  bottom: "Dropout16"
  top: "Concat15"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm17"
  type: "BatchNorm"
  bottom: "Concat15"
  top: "BatchNorm17"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale17"
  type: "Scale"
  bottom: "BatchNorm17"
  top: "BatchNorm17"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU17"
  type: "ReLU"
  bottom: "BatchNorm17"
  top: "BatchNorm17"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution18"
  type: "Convolution"
  bottom: "BatchNorm17"
  top: "Convolution18"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout17"
  type: "Dropout"
  bottom: "Convolution18"
  top: "Dropout17"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat16"
  type: "Concat"
  bottom: "Concat15"
  bottom: "Dropout17"
  top: "Concat16"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm18"
  type: "BatchNorm"
  bottom: "Concat16"
  top: "BatchNorm18"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale18"
  type: "Scale"
  bottom: "BatchNorm18"
  top: "BatchNorm18"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU18"
  type: "ReLU"
  bottom: "BatchNorm18"
  top: "BatchNorm18"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution19"
  type: "Convolution"
  bottom: "BatchNorm18"
  top: "Convolution19"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout18"
  type: "Dropout"
  bottom: "Convolution19"
  top: "Dropout18"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat17"
  type: "Concat"
  bottom: "Concat16"
  bottom: "Dropout18"
  top: "Concat17"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm19"
  type: "BatchNorm"
  bottom: "Concat17"
  top: "BatchNorm19"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale19"
  type: "Scale"
  bottom: "BatchNorm19"
  top: "BatchNorm19"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU19"
  type: "ReLU"
  bottom: "BatchNorm19"
  top: "BatchNorm19"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution20"
  type: "Convolution"
  bottom: "BatchNorm19"
  top: "Convolution20"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout19"
  type: "Dropout"
  bottom: "Convolution20"
  top: "Dropout19"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat18"
  type: "Concat"
  bottom: "Concat17"
  bottom: "Dropout19"
  top: "Concat18"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm20"
  type: "BatchNorm"
  bottom: "Concat18"
  top: "BatchNorm20"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale20"
  type: "Scale"
  bottom: "BatchNorm20"
  top: "BatchNorm20"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU20"
  type: "ReLU"
  bottom: "BatchNorm20"
  top: "BatchNorm20"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution21"
  type: "Convolution"
  bottom: "BatchNorm20"
  top: "Convolution21"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout20"
  type: "Dropout"
  bottom: "Convolution21"
  top: "Dropout20"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat19"
  type: "Concat"
  bottom: "Concat18"
  bottom: "Dropout20"
  top: "Concat19"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm21"
  type: "BatchNorm"
  bottom: "Concat19"
  top: "BatchNorm21"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale21"
  type: "Scale"
  bottom: "BatchNorm21"
  top: "BatchNorm21"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU21"
  type: "ReLU"
  bottom: "BatchNorm21"
  top: "BatchNorm21"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution22"
  type: "Convolution"
  bottom: "BatchNorm21"
  top: "Convolution22"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout21"
  type: "Dropout"
  bottom: "Convolution22"
  top: "Dropout21"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat20"
  type: "Concat"
  bottom: "Concat19"
  bottom: "Dropout21"
  top: "Concat20"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm22"
  type: "BatchNorm"
  bottom: "Concat20"
  top: "BatchNorm22"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale22"
  type: "Scale"
  bottom: "BatchNorm22"
  top: "BatchNorm22"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU22"
  type: "ReLU"
  bottom: "BatchNorm22"
  top: "BatchNorm22"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution23"
  type: "Convolution"
  bottom: "BatchNorm22"
  top: "Convolution23"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout22"
  type: "Dropout"
  bottom: "Convolution23"
  top: "Dropout22"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat21"
  type: "Concat"
  bottom: "Concat20"
  bottom: "Dropout22"
  top: "Concat21"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm23"
  type: "BatchNorm"
  bottom: "Concat21"
  top: "BatchNorm23"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale23"
  type: "Scale"
  bottom: "BatchNorm23"
  top: "BatchNorm23"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU23"
  type: "ReLU"
  bottom: "BatchNorm23"
  top: "BatchNorm23"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution24"
  type: "Convolution"
  bottom: "BatchNorm23"
  top: "Convolution24"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout23"
  type: "Dropout"
  bottom: "Convolution24"
  top: "Dropout23"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat22"
  type: "Concat"
  bottom: "Concat21"
  bottom: "Dropout23"
  top: "Concat22"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm24"
  type: "BatchNorm"
  bottom: "Concat22"
  top: "BatchNorm24"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale24"
  type: "Scale"
  bottom: "BatchNorm24"
  top: "BatchNorm24"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU24"
  type: "ReLU"
  bottom: "BatchNorm24"
  top: "BatchNorm24"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution25"
  type: "Convolution"
  bottom: "BatchNorm24"
  top: "Convolution25"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout24"
  type: "Dropout"
  bottom: "Convolution25"
  top: "Dropout24"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat23"
  type: "Concat"
  bottom: "Concat22"
  bottom: "Dropout24"
  top: "Concat23"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm25"
  type: "BatchNorm"
  bottom: "Concat23"
  top: "BatchNorm25"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale25"
  type: "Scale"
  bottom: "BatchNorm25"
  top: "BatchNorm25"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU25"
  type: "ReLU"
  bottom: "BatchNorm25"
  top: "BatchNorm25"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution26"
  type: "Convolution"
  bottom: "BatchNorm25"
  top: "Convolution26"
  convolution_param {
    engine:CUDNN
    num_output: 12
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Dropout25"
  type: "Dropout"
  bottom: "Convolution26"
  top: "Dropout25"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Concat24"
  type: "Concat"
  bottom: "Concat23"
  bottom: "Dropout25"
  top: "Concat24"
  concat_param {
    axis: 1
  }
}
layer {
  name: "BatchNorm26"
  type: "BatchNorm"
  bottom: "Concat24"
  top: "BatchNorm26"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale26"
  type: "Scale"
  bottom: "BatchNorm26"
  top: "BatchNorm26"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU26"
  type: "ReLU"
  bottom: "BatchNorm26"
  top: "BatchNorm26"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Convolution27"
  type: "Convolution"
  bottom: "BatchNorm26"
  top: "Convolution27"
  convolution_param {
    engine:CUDNN
    num_output: 304
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
############################
layer {
  name: "Deconv1"
  type: "Deconvolution"
  bottom: "Convolution27"
  top: "Deconv1"
  convolution_param {
    kernel_size: 4
    stride: 2
    num_output: 128
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      std: 0
    }
    engine:CUDNN
  }
}
layer {
  name: "relu_d1"
  type: "ReLU"
  bottom: "Deconv1"
  top: "Deconv1"
  relu_param {
    engine:CUDNN
  }
}
layer {
  name: "Deconv2"
  type: "Deconvolution"
  bottom: "Deconv1"
  top: "Deconv2"
  convolution_param {
    kernel_size: 4
    stride: 2
    num_output: 64
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      std: 0
    }
    engine:CUDNN
  }
}

layer {
  name: "relu_d2"
  type: "ReLU"
  bottom: "Deconv2"
  top: "Deconv2"
  relu_param {
    engine:CUDNN
  }
}

layer {
  name: "score_main1"
  type: "Convolution"
  bottom: "Deconv2"
  top: "score_main"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 3
    kernel_size: 1
    engine:CUDNN
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      std: 0
    }
  }
}
layer {
  name:"loss_main"
  type: "SoftmaxWithLoss"
  bottom: "score_main"
  bottom: "label"
  loss_weight: 1
  top: "loss_main"
  softmax_param{
	engine:CUDNN
  }
}

#===
layer {
  name: "score_aux1"
  type: "Convolution"
  bottom: "Convolution14"
  top: "score_aux1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 3
    kernel_size: 1
    engine:CUDNN
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      std: 0
    }
  }
}
layer {
  name: "upscore_aux1"
  type: "Deconvolution"
  bottom: "score_aux1"
  top: "upscore_aux1"
  convolution_param {
    kernel_size: 4
    stride: 2
    num_output: 3
    group: 3
    pad: 1
    weight_filler { 
    type: "bilinear_3D"
    }
    bias_term: false
    engine:CUDNN
  }
  param { 
    lr_mult: 0.1 
    decay_mult: 1 
  }
}
layer {
  name:"loss_aux1"
  type: "SoftmaxWithLoss"
  bottom: "upscore_aux1"
  bottom: "label"
  loss_weight: 0.33
  softmax_param{
	engine:CUDNN
  }
}